Pixellib is a library for performing segmentation of objects in images and videos. It supports the two major types of image segmentation:

1.Semantic segmentation

2.Instance segmentation

Install PixelLib and its dependencies

Install Tensorflow:

Install latest version of tensorflow(Tensorflow 2.0+) with:

pip3 install tensorflow

If you have have a pc enabled GPU, Install tensorflow--gpu's version that is compatible with the cuda's version on your pc:

pip3 install tensorflow--gpu

Install imgaug with:

pip3 install imgaug

Install Pixellib with:

pip3 install pixellib --upgrade

Visit PixelLib's official documentation on readthedocs

The following features are supported for background editing,

1.Change image background with picture

2.Assign a distinct color to the background of an image and a video

3.Blur the background of an image and a video

4.Grayscale the background of an image and a video

There are two types of Deeplabv3+ models available for performing semantic segmentation with PixelLib:

1. Deeplabv3+ model with xception as network backbone trained on Ade20k dataset, a dataset with 150 classes of objects.
2. Deeplabv3+ model with xception as network backbone trained on Pascalvoc dataset, a dataset with 20 classes of objects.

Instance segmentation is implemented with PixelLib by using Mask R-CNN model trained on coco dataset.

The latest version of PixelLib supports custom training of object segmentation models using pretrained coco model.

Note: PixelLib supports annotation with Labelme. If you make use of another annotation tool it will not be compatible with the library. Read this tutorial on image annotation with Labelme.

• Instance Segmentation of Images With PixelLib Using Mask-RCNN COCO Model

• Instance Segmentation of Videos With PixelLib Using Mask-RCNN COCO Model

• Custom Instance Segmentation Training

• Instance Segmentation of objects in images and videos With Custom Model

• Semantic Segmentation of Images With PixelLib Using Ade20k model

• Semantic Segmentation of Videos With PixelLib Using Ade20k model

• Semantic Segmentation of Images With PixelLib Using Pascalvoc model

• Semantic Segmentation of Videos With PixelLib Using Pascalvoc model

Note Deeplab and mask r-ccn models are available in the release of this repository.

Learn how to implement state of the art instance segmentation of objects with Mask-RCNN with PixelLib using 5 Lines of Code.

Implement state of the art instance segmentation of objects in video's feeds with Mask-RCNN model using 5 Lines of Code.

Perform instance segmentation on objects with your custom model with 7 Lines of Code.

Perform inference on objects with your custom model.

Learn how to perform state of the art semantic segmentation of 150 classes of objects with Ade20k model using 5 Lines of Code. Perform indoor and outdoor segmentation of scenes with PixelLib by using Ade20k model.

Implement state of the art semantic segmentation of 150 classes objects in video's feeds using Ade20k model with PixelLib using 5 Lines of Code.

Learn how to perform state of the art semantic segmentation of 20 common objects with Pascalvoc model using 5 Lines of Code. Perform segmentation of unique objects with PixelLib by using Pascalvoc model.

Implement state of the art semantic segmentation of 20 unique objects in video's feeds using Pascalvoc model with PixelLib using 5 Lines of Code.

1. A segmentation api integrated with PixelLib to perform Semantic and Instance Segmentation of images on ios https://github.com/omarmhaimdat/segmentation_api

2. PixelLib is integerated in drone's cameras to perform instance segmentation of live video's feeds https://elbruno.com/2020/05/21/coding4fun-how-to-control-your-drone-with-20-lines-of-code-20-n/?utm_source=twitter&utm_medium=social&utm_campaign=tweepsmap-Default

3. PixelLib is used to find similar contents in images for image recommendation https://github.com/lukoucky/image_recommendation

1. Bonlime, Keras implementation of Deeplab v3+ with pretrained weights https://github.com/bonlime/keras-deeplab-v3-plus

2. Liang-Chieh Chen. et al, Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation https://arxiv.org/abs/1802.02611

3. Matterport, Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow https://github.com/matterport/Mask_RCNN

4. Mask R-CNN code made compatible with tensorflow 2.0, https://github.com/tomgross/Mask_RCNN/tree/tensorflow-2.0

5. Kaiming He et al, Mask R-CNN https://arxiv.org/abs/1703.06870

6. TensorFlow DeepLab Model Zoo https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md

7. Pascalvoc and Ade20k datasets' colormaps https://github.com/tensorflow/models/blob/master/research/deeplab/utils/get_dataset_colormap.py

8. Object-Detection-Python https://github.com/Yunus0or1/Object-Detection-Python

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